Evidence of hatch‐time based growth compensation in the early life history of two salmonid fishes

Abstract Initial body size can indicate quality within‐species, with large size increasing the likelihood of survival. However, some populations or individuals may have body size disadvantages due to spatial/temporal differences in temperature, photoperiod, or food. Across‐populations, animals often have locally adapted physiology to compensate for relatively poor environmental influences on development and growth, while within‐population individual behavioral adjustments can increase food intake after periods of deprivation and provide opportunities to catch up (growth compensation). Previous work has shown that growth compensation should include within‐population differences related to short growing seasons due to delayed hatch time. We tested the hypothesis that individual fish that hatch later grow faster than those that hatch earlier. The relative magnitude of such a response was compared with growth variation among populations. We sampled young of the year Arctic charr and brook trout from five rivers in northern Labrador. Daily increments from otoliths were used to back‐calculate size to a common age and calculate growth rates. Supporting the hypothesis, older fish were not larger at capture than younger fish because animals that hatched later grew faster, which may indicate age‐based growth compensation.

Intra-specific variation in growth rate is ubiquitous and occurs at population (e.g., Carlson et al., 2004;McCairns, 2004;Yamahira & Conover, 2002) and individual levels (e.g., Mortensen & Damsgård, 1993;Nicieza & Metcalfe, 1997). Among-population growth differences often exist due to latitudinal (and elevational) gradients in temperature and photoperiod, with individuals at higher latitudes experiencing shorter growing seasons (Campos et al., 2009;Sinnatamby et al., 2014). If phenotypic optima are similar across such conditions, disadvantaged populations can evolve a greater genetic capacity for growth to mitigate some negative environmental effects on size via plasticity (Arendt & Wilson, 1999;Campos et al., 2009;Conover & Present, 1990;Pearson & Warner, 2018;Purchase & Brown, 2000). There is predictable latitudinal variation in temperature and photoperiod that contribute to counter-gradient variation in growth potential among populations, where higher latitude populations evolve faster growth rates at equivalent temperatures to that of those at lower latitudes (Campos et al., 2009;Lapolla, 2001;Sinnatamby et al., 2014). Such among-population patterns in local adaptation are termed counter-gradient variation and occur via genetic changes across generations (Figure 1).
Within-populations, some individuals may experience relatively disadvantaged conditions. For example, a period of depressed feeding opportunities can result in diminished growth rates; however, compensatory behaviors can allow them to catch up by the end of the growing season (Metcalfe & Monaghan, 2001). Whereas counter-gradient variation is genetic adaptation across-populations, within-population growth compensation ( Figure 1) may be considered an intrinsic plastic response (of the same individual) to changes in the environment (Carlson et al., 2004;Zhu et al., 2003) that is triggered by environmental cues (Gotthard, 2008), depletion of energy stores (Ali et al., 2003), or timing of hatch (Gotthard, 2000;Hirose et al., 2012;Mikolajewski et al., 2015;Orizaola et al., 2010;Simonin et al., 2016;Stoks et al., 2006). Compensatory growth can have positive effects on individuals, through an increased likelihood of survival (associated with larger body size); however, growth compensation is associated with bolder foraging behaviors, which put these individuals at a greater risk of predation (Biro et al., 2004;Damsgård & Dill, 1998;Nicieza & Metcalfe, 1997).
Among-populations, in addition to growth potential, countergradient variation in local adaptation has been associated with developmental timing ( Figure 1); the concept is easily transferable.
Both are influenced by temperature, and locally adapted physiology can produce common phenotypes despite substantial plasticity.
Salmonid fishes provide a useful illustration. Salmonids are poikilothermic meaning their developmental rate is linked to temperature, hatching faster in warmer water. However, at colder temperatures, they need fewer accumulated thermal units (ATU) to hatch (Brannon, 1987;Quinn, 2005), and there is evidence of countergradient variation of ATUs to hatch, across-populations (Sparks et al., 2019).
Sub-optimal hatching phenology can result in a mismatch in trophic dynamics with prey (Brännäs, 1995), whereby food is unavailable to newly hatched offspring. Optimal hatching timing is stochastic year to year but may be relatively stable across generations. Hatch too early and there may be no food and/or sub-optimal environmental conditions. However, late hatchers are at a competitive disadvantage for feeding territories compared with early hatchers due to dominance hierarchies (Cutts et al., 1999;Metcalfe & Thorpe, 1992).
Previous work has established that subsequent growth rate is related to hatch time across-populations (e.g., Lapolla, 2001), and within-individuals, periods of slow growth due to limited food will be compensated by periods of faster growth when food availability increases (Metcalfe & Monaghan, 2001). In this study, we focus on growth compensation within-populations related to shorter growing seasons due to delayed hatch time ( Figure 1). An individual may compensate for hatching late, where they are disadvantaged by a shorter growing season, by growing faster than other individuals (within their population) that hatched earlier, thereby making the best of a bad situation. Such work has been supported by both theory (Abrams et al., 1996) and data from insects (see: Gotthard, 2000;Mikolajewski et al., 2015;Stoks et al., 2006), fish (Simonin et al., 2016), amphibians (Orizaola et al., 2010), and birds (Hirose et al., 2012;Lindholm et al., 1994). We tested this hypothesis in two salmonid species (Salvelinus spp.) where the relative magnitude of such a response was compared with growth variation within each species and across their populations (five rivers) in northern Labrador, Canada.

| Environmental information
Sampling occurred on secondary and tertiary streams of five river systems in northern Labrador, Canada: Hebron River, Kamanatsuk Brook, Fraser River, Anaktalik Brook, and Igluvigaluk Brook ( Figure 2; Appendix S1). Temperature loggers (n = 4, HOBO TidbiT v2, UTBI-001) were installed during the spawning season in October 2012 and removed during the June 2013 sampling period at two sampling sites, Fraser and Anaktalik rivers; and one additional river: Ikadlivik Brook (Appendix S1). Loggers were fastened to rebar and firmly placed in riverbeds (Appendix S2). Salmonids often spawn in groundwater seeps having relatively steady flows of water at stable temperatures. The temperature loggers placed in Fraser River were in a spawning aggregation where redds were observed, while the loggers in Anaktalik River and Ikadlivik Brook were placed in the main flow of the river. However, the temperature estimates from our loggers are likely underestimates (through winter) compared to those experienced in the redds because the loggers were in the water column and not in the gravel (where salmonids lay their eggs, and when in the presence of groundwater seeps, tend to have more stable temperatures). Average temperatures per day were plotted (Appendix S2). Temperatures below 0°C were not included in the overall monthly averages because the loggers were assumed to be in ice.

| Fish collection
We collected young of the year Arctic charr and brook trout (Salvelinus alpinus and S. fontinalis) using a Smith-Root LR24 backpack electrofisher between June 24 and June 29, 2013. Potential collection sites were viewed from a helicopter with each tributary selectively sampled by electrofishing upstream. Areas where we were unlikely to find young of the year, such as sandy substrate, turbulent water, or water that was deeper than ~1 m, were not surveyed. The minimum stretch of water sampled was ~100 m long by at least 1 m wide per stream (minimum area 100 m 2 per river), and we only sampled two or three individuals from any one group of fish to minimize the risk of collecting multiple siblings from a family. After capture, the fish were euthanized, measured (fork length), and a tissue sample was taken and placed in 95% ethanol for genetic species identification. The fish were then frozen (at −20°C) for later otolith extraction.
All animals were handled in accordance with the Guide to the Care and Use of Experimental Animals (Memorial University Animal care permit: 12-08-CP) and permitting from Fisheries and Oceans Canada.

| Genetic identification of species
The small physical size of the newly emerged hatchlings made morphological species identification difficult. Therefore, we used genetic barcoding to identify the species of each individual (brook trout or Arctic charr; n = 436). We extracted DNA from tissue samples using a Qiagen DNeasy Blood and Tissue Kit according to the manufacturer's protocol (Qiagen).
A 520 base pair fragment of the cytochrome c oxidase 1 (CO1) gene was amplified by PCR using standard COI barcoding primers (Cox1-1F AACGTAATTGTCACCGCCCATG and Cox1-1R CACCTCAGGGTGTCCGAAG-AAT). We purified the PCR products with an Exo-SAP clean-up method and sent them to Genome F I G U R E 1 Schematic showing the theoretical development (embryos) and growth (juveniles) rate responses to disadvantaged conditions among-(local adaptation) and within-(physiological compensation) populations.
Quebec (McGill University, QC) for sequencing using standard dideoxy methods. We aligned the sequences in MEGA v6.0 (Tamura et al., 2013) and species identification was unambiguously determined for all 436 fish.

| Otolith work and hatch date
Sagittal otoliths were extracted from young of the year fish using an established methodology (Radtke et al., 1996). Each otolith was fixed to a glass slide and polished using 3 and 30 μm lapping film. In salmonids a layer of calcium carbonate is deposited every day; this forms an increment that can be used to interpret fish age (see Radtke, 1989;Radtke et al., 1996;and Adams et al., 1992 for methods). When a band appears darker and thicker than the others, it is considered a check. Checks can occur for a variety of reasons including stress due to hatch, emergence, or environmental change such as a storm, lack of food, or handling stress in aquaculture settings (Adams et al., 1992;Campana & Neilson, 1985). The result of this stress is slower growth, and therefore two or more daily rings merge into one thicker ring (Adams et al., 1992). In this study, we were interested in both hatch and emergence checks. The hatch check is a thick ring that encircles all of the primordia (nuclei upon which the otolith is built) near the core of the otolith. The emergence check occurs when hatchlings leave their gravel nest and begin exogenous feeding (Appendix S3).
After the establishment of the emergence check, growth often accelerates and therefore subsequent rings are further apart and more translucent (Campana, 2001;Campana & Neilson, 1985). Other work has shown that hatch and emergence times can be inferred in wild salmonids based on otolith microstructure (Fitzgerald et al., 2021).
Of the original 436 individuals, only fish from rivers with a sample size n ≥ 10 young of the year fish of a species and for which we could obtain daily age readings were included in further analyses (324 fish: 206 Arctic charr; 118 brook trout; see Appendix S4 for details). We were unable to age 112 fish due to otolith loss or breakage during processing. Based on the number of daily increments present and the date of capture, each fish's hatch date was back-calculated (Table 1). Photographs of otoliths were taken using a compound microscope under 100× magnification. The photographs were cropped and gray-scaled, and the color range of grays was reduced to make ring visualization easier using Photoshop.
Each otolith was assigned a blind code and read without knowledge of species or river origin. We reinterpreted age on a random subset of 50 fish to determine precision. Precision estimates were based on the coefficient of variation (CV) values (Chang, 1982). A F I G U R E 2 (a) Map of Newfoundland and Labrador, Canada; (b) inset of study area. For both maps, points indicate sampling sites (circles for fish collection, triangles for temperature loggers). See Table S1 for site details and GPS locations. review found that a CV of less than ~8% is generally acceptable for aging studies (Campana, 2001). Our precision estimate was 7.1% for days posthatch and 8.0% for days postemergence. However, when we compared the 40 emergence checks in the 50 subsampled otoliths to the original interpretation, 17.5% (n = 7) did not agree (i.e., a check was noted in one reading but not the other). Therefore, determining emergence checks was deemed unreliable for these otoliths, and we did not examine emergence time in further analyses. All otoliths were aged by the same reader (HDP).

| Growth rate and back-calculated lengths
We used the ObjectJ plugin for ImageJ (Schneider et al., 2012) to calculate the fish's daily growth rate (fork length in mm/day) based on the width of the daily otolith rings. The total radius of the otolith was measured from the hatch line to the last visible increment.
Absolute daily growth was then calculated based on the total growth of the fish (fork length at capture minus estimated hatch size; defined below) compared with the width of each daily otolith ring.
Where L was fish fork length (size, mm), O was otolith radius (μm), L c and O c were sizes at capture, L a and O a were sizes at age, and L o and O o were sizes at hatch. One of the weaknesses of this model was that hatch fork length needs to be estimated in order to estimate posthatch lengths. We used a hatch fork length (L o ) of 18 mm based on previous work on brook trout (Penney et al., 2018). Additionally, we conducted a sensitivity analysis with an assumed hatch size of 16 and 20 mm and it made no difference in the overall conclusions.
We calculated each fish's size (fork length in mm) each day based on the width of each otolith ring. Next, an average daily growth rate (average fork length increase, mm/day) was calculated based on the total absolute growth of the fish (length at capture minus estimated hatch size, mm, see model 1) compared with the width of each daily otolith ring. We compared growth rates (mm/day) for three different time periods. First, we examined the first 25 days of life to compare age effects on growth, next we examined a set time period (from June 1 to 21) to compare growth rates during standardized environmental conditions (photoperiod, and temperature), and finally, we looked at overall growth rates (posthatch to time of capture). For each of these instances, we used model (1) to estimate the growth rate.

| Data analyses and statistics
For descriptive purposes and to determine whether we could examine our hypothesis at a species level, we tested whether the river Note: Site number refers to locations in Figure 1. Descriptive statistics for: Mean age (in days, on June 24-First day of electrofishing), mean and median hatch date, mean otolith radius at capture (mm), otolith radius at hatch (mm), fork length at capture (mm), and estimated fork length at 25 and 50 days old (mm). Only samples from species in rivers that had a sample size of at least 10 individuals were included (N, sample size of aged fish; SD, standard deviation).
(population) had an effect on any of our dependent variables (hatch date, growth rate, fork length; Table 2). To determine whether hatch date (HD, taken from age on June 24) or overall growth rate (GR, mm/day, posthatch to time of capture) differed between species (Sp) or among rivers (R), we conducted two analyses of variance (ANOVA) using model (2). Since there were no differences in growth rates in the first 25 days, the 3 weeks in June, or the overall growth rate we chose the overall growth rates in our analysis.
We did not test for the interaction between species and river for model (2) because we did not have representatives from both species in each river. There was no effect of the river on hatch date (above), or overall growth rate (Tables 1 and 2), so the populations were pooled for further analyses. Therefore, we conducted linear regressions for each species (regardless of the river) to determine: (1) whether there was an association between fork length at capture and age and (2) whether there was an association between hatch date and growth rate. For all analyses, α was set at .05. Residuals were examined to test for normality and heteroscedasticity, and no deviations were observed. The map was created in ArcGIS. All graphs (ggplot2), data processing, and statistics were done in R version 4.1.2 (R Core Team, 2021; using packages car, ggpmisc, Hmisc, lme4, and lubridate).
Subsequent to pooling populations, we conducted linear regressions and found no relationship between age and fork length at capture (mm) for Arctic charr (R 2 = .003, p = .42) or brook trout (R 2 = .007, p = .38; Figure 4). Older fish were not bigger than younger fish. To better understand the absence of a relationship between age and body size at the same capture time, we conducted additional tests regressing hatch date to the daily growth rate (fork length increase, mm/day) at three time points.

| DISCUSS ION
The objective of this paper was to test the hypothesis that hatch time affects growth rate, where individuals that hatch later are disadvantaged by a shorter growing season than those that hatch earlier and therefore would grow faster to compensate, potentially making the best of a bad situation. We found support for our hypothesis in two species of salmonids. Overall, there was more variation in growth rate among individuals within-populations, than across-populations.
There was no relationship between age (in days) and size of young of the year charrs (>75 days old, sampled in late June), which means that older hatchlings were not larger than younger ones. We found that this occurred because fish that hatched later grew faster, potentially as a form of growth compensation. Interestingly, this finding is contrary to work conducted on Northern pike (Esox lucius) and common triplefin (Fosterygion lapillum) where early hatchers grew faster (Moginie & Shima, 2018;Trabelsi et al., 2013, respectively); and work on congeneric white-spotted charr (S. leucomaensis) where early hatchers were consistently larger than late hatchers (Yamamoto et al., 1997).
Individuals may have poor resource stores if they have few opportunities to gather resources. This situation can arise through a food shortage brought on by a competitive disadvantage from not establishing feeding territories before others in their cohort (Cutts et al., 1999;Metcalfe & Thorpe, 1992), or shorter growing seasons (Arendt & Wilson, 1999;Campos et al., 2009). A period of faster (compensatory) growth often occurs after depletion of resources causes a period of slow growth (Ali et al., 2003;Metcalfe & Monaghan, 2001).
Individuals counteract or compensate for this disadvantage by growing faster, which allows them to reach a similar size at a later time point, to individuals that were not stunted. This has been shown in juvenile Atlantic salmon, where there was a negative relationship (2) HD or GR ∼ Sp + R + error TA B L E 2 Results of analysis of variance (ANOVA) for hatch date and overall posthatch growth rate (mm/day) between fish species (Salvelinus alpinus and S. fontinalis) for samples collected among five rivers June 24 to June 29, 2013, in Labrador, Canada. between size at the end of the final winter before smolting and growth rate up to the smolt migration (Nicieza & Brana, 1993).
To investigate this process in response to hatch timing instead of food, we examined growth rates at three time points: during initial growth, for the entire life posthatch, and for the first 3 weeks of June. We found that during all three time periods individuals that hatched later grew faster than early hatchers. This phenomenon was observed in daily otolith growth increments, where incremental growth of late-hatching individuals tended to be larger than that of early-hatching individuals. The June growth comparisons indicate that late hatchers grow faster even under the same abiotic conditions and food availability (discussed below).
Previous work in other salmonid species has shown that larger eggs tend to produce bigger offspring, and larger offspring may emerge from the nest (i.e., ready to begin exogenous feeding) earlier than smaller offspring (e.g., Cogliati et al., 2018;Solberg et al., 2014). The probable difference in resource availability (both diminished fat stores and yolk resources) in early life may be enough to trigger a growth compensation response in the late-hatching fish.
Additionally, individuals that are larger have a higher absolute growth rate but a lower relative or proportional growth rate (Van Buskirk et al., 2017). Cogliati et al. (2018) found that when comparing early and late hatchers there was no difference in growth rate but did find that fish from small eggs had a significantly larger increase in size over time. In our case, the older fish grew slower, therefore the potential bias is in a conservative direction because the small fish had a higher absolute growth rate and a higher proportional growth rate.
We do not know whether there was differential survival based on size or growth rate, therefore it is possible that individuals that hatched late but grew slow may have had higher mortality.
Additionally, it is unknown with certainty whether intrinsic effects such as hatch time, or extrinsic environmental conditions experienced at different hatch times, were the main causes for differences in growth rate. There are likely notable differences in food supply (e.g., insect abundance increases through the spring) and photoperiod in the experiences of the early and late hatchers, which might explain the differences in growth rates. One could also assume that temperature would be an extrinsic explanation for the pattern of increased growth rate later in the season.  (Clarke et al., 2016). Future work designed to empirically test specifically how hatch phenology affects growth rate and survival through the first year of life, and how that translates into differences in fitness for salmonids is recommended. Furthermore, predicted changes in climate are likely to affect hatch phenology (Rooke et al., 2019), and therefore, more research should be done to understand the consequences of changes in complex northern ecosystems.

ACK N OWLED G M ENTS
The authors would like to thank I.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data will be archived on Dryad upon publication.